细胞生物学
计算生物学
有丝分裂
核糖体
细胞周期
翻译(生物学)
细胞
生物
单细胞分析
人口
氨基酸
核糖体分析
电池类型
限制
遗传学
信使核糖核酸
基因
核糖核酸
人口学
社会学
工程类
机械工程
作者
Michael VanInsberghe,Jeroen van den Berg,Amanda Andersson-Rolf,Hans Clevers,Alexander van Oudenaarden
出处
期刊:Nature
[Springer Nature]
日期:2021-09-08
卷期号:597 (7877): 561-565
被引量:104
标识
DOI:10.1038/s41586-021-03887-4
摘要
Single-cell sequencing methods have enabled in-depth analysis of the diversity of cell types and cell states in a wide range of organisms. These tools focus predominantly on sequencing the genomes1, epigenomes2 and transcriptomes3 of single cells. However, despite recent progress in detecting proteins by mass spectrometry with single-cell resolution4, it remains a major challenge to measure translation in individual cells. Here, building on existing protocols5–7, we have substantially increased the sensitivity of these assays to enable ribosome profiling in single cells. Integrated with a machine learning approach, this technology achieves single-codon resolution. We validate this method by demonstrating that limitation for a particular amino acid causes ribosome pausing at a subset of the codons encoding the amino acid. Of note, this pausing is only observed in a sub-population of cells correlating to its cell cycle state. We further expand on this phenomenon in non-limiting conditions and detect pronounced GAA pausing during mitosis. Finally, we demonstrate the applicability of this technique to rare primary enteroendocrine cells. This technology provides a first step towards determining the contribution of the translational process to the remarkable diversity between seemingly identical cells. Highly sensitive ribosome profiling of single cells at single-codon resolution enables identification of distinct cell cycle-dependent translational dynamic states in individual cells.
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